Agentic AI Everywhere: The Future of Autonomous Intelligence?

And I think that will be quite beneficial for society in general because there we don't have enough people to get the work done.

We'll get into ethical considerations. You know, we talked a little bit about I sent you a, a link to a paper that suggests that maybe we shouldn't have completely autonomous systems. So have you weigh in on that. And with that, I'd like to welcome Neetu, Pathak. She's CEO and co-founder of SkyMel, and Drew Dimmick, CTO and co-founder of Prompt 360. Welcome.

If I were to average out everything, I searched on the internet, everything the LLMs tell me about Ag agentic. It's autonomous AI systems that act independently. In dynamic environments to achieve specific goals. Like that's a very academic definition of what it is and what it does. But you know, like, so we think about agents, like what are some of the things, Viby, that we would think about in that case? I.

And then the third thing is like they are context aware. They interact with the environment the tools, et cetera, and they're capable of learning as well as adapting from those interactions. So those are what I would call as like the three main characteristics. Of an agent system, so they're just not responding to a prompt, but actively working towards the human directed goal through a multi-step process.

Even if you look at so there, there was a paper which was talking about BID or it was PDI. So it was belief, desire, and influence. Yes, it was BDI. So, um, that was like the first concept of what we see today. I recently wrote a guest article where I kind of combined five different actions that agent needs to do. So first is called perceive. That means you can see the environment you are in. And then reason I. That means you can collect all the data and kind of make sense of it.

What does it mean, what the intent is? And also understand anomalies and constraints. One of the things people don't talk about an agent is, oh, you have a goal, but you still have to work within a constraint. Like if I ask an agent, Hey, can you book me a ticket from SF to India? Obviously I'll have some constraint in terms of money stops. So people don't talk about that. Every agent has to work within a constraint, so that's important. The next is obviously plan.

So now you have all the data, you have kind of your constraints and you go, uh, you have your goal. Now you are coming up with different decisions of how you can reach there. And once you decide the right path, then you have to act on it. And once you act, then you ha need to have an ability to monitor did you do it right or did you do it wrong? And if something went wrong, what went wrong? And you learn from it. And it's a feedback loop.

So, and this is not my definition, I just read a lot of paper to kind of combine everything together. What would a agent look like?

And we are re remembering those so that when the next user comes along you're able to harvest that data. for additional queries, and that may be completely different intense, but need the same source data. So those the long-term memory and then the embedded knowledge graphs that we're looking at doing here are a huge part of the delivery that up the agent that is our solution alone, which is kind of interesting.

Almost everybody that's doing agent-based work agents themselves to other agents, and you've got this compounding effort on, which is which is powerful. And we don't e we haven't even seen the beginning of the potential there.

You can't forget everything you just did today that I trained you on. And then you start to learn and you get better at your job. So I think that's an important point. like, if I were to compare that to an LLM, like a chat GPT or a Claude, I can go interact with them. And today I can have them do some things. I can have it create a Word doc for me. So how is it different from a traditional LLM that I would interact with?

So they want to take advantage of the training and the work that the large models are done, then it may do some fine tuning and get a slice of that model to use internally, but they're not feeding that information back and they see that as an information security risk actually. and and a data governance risk that needs to be really closely monitored. So that's kind of given us impetus to look at other ways for us to do that.

And conveniently, the ag agent approach with the memory facilities that we have inside of our system allows the customers to completely control all that memory information. Within their four walls whether that be in a VPC on a cloud or on a on-prem system, that's kind of irrelevant, but it's under their control. And so the risk of data scape is much, much less. And it lends to the notion of agentic topologies in general because wanna have separation of concerns for those kinds of things.

And that is a needs will be a continued pattern inside of the enterprise. That goes back to good practices for data security across most enterprise environments is, you know, zero trust models. Only people that have access, have need can get access to things and just do that from a design footprint. And AG agentic models need to be following that in order to be successful inside of large enterprises, but they're also quite powerful and delivering all these capabilities.

I think on the application side, what I would say is like we read in the industry about Morgan Stanley's internal advisor agent, right? Where you're like getting. Financial analysts with supporting agents, supporting them with complex queries. We read about like software tools like PR agent that conduct code reviews.

So we are seeing a lot of these implementations are not simple theoretical constructs, but more of like operational systems that are driving efficiency gains that are measurable and also where mistakes carry real consequences, right? More or less the industry is pointing that like most of the efficiencies as well as that come from these applications are in specialized domains where the stakes are highest.

One big example that's being quoted is like Toyota's multi-agent system claiming reduction in production planning by 71%, right? That's a huge number. Diagnostic assistance in clinic, clinical decisions augmenting expert judgment. So I'm curious as to, these are sort of like what we read in the industry as well as sort of like being quoted. to hear more from Knee to end drew us to some of the examples that they're seeing.

Then that would be one area that we could explore in terms of what are some of the real world as well as, especially in the IT world, what are some of the examples that we are actually seeing being operationalized?

But if you create an agent that goes and scrape a web, kind of creates a profile of the person that they're. Sending to have an idea about what kind of this profile would respond to and then create an email that's just code running 24 7. But a human cannot do that. The agents are coming handy where you either want to reduce the time it takes to deliver something or increase the revenue that comes out of it, even if there is a little bit of mistake.

Like even if you make five person mistake out of a hundred people, five person didn't like the personalized email you sent, but you are getting nine five, that's still better than industry standard right now.

Uh, so it really depends upon use case to use case and what you're trying to achieve using agent ai.

You may also have security concerns around certain versions being vulnerable or an, or have have toxic licenses, for example. And these are all things that people that are building out enterprise applications have to deal with on a day-to-day basis. It's really arcane, slow, hard to do work that's necessary, and it creates huge inefficiencies inside of their develop, development and value delivery chains they don't do it right.

So, for example, one of our customers had a POS system going into place. And they weren't keeping track of their software supply chain and all of the different aspects of that. And it turns out that one of the components that they had inside embedded in the system, was going obsolete. And when they got to release time, they didn't know that it was going obsolete until they just were about to ship it.

And then they had to pull back and go back and completely rewrite their stack because it was a very, very essential part of their component architecture. And, so that probably cost them months in development time to go remediate that. Our approach is that hey, let's automate all that stuff and have it done, you know, on demand or nightly or weekly.

Take a look at where you are with your development stuff, compare it to your standards, compare it to your vulnerabilities and software supply chains and contracts, and make sure that. You what you're building is gonna be shippable. That's just one of the use cases, but this one is huge amounts of savings. We, by our estimates, it's somewhere around 80% savings compared to the typical work that's done in this. Just doing the data gathering and the doing.

The comparison of that is, is takes weeks. So if people don't do it well then they end up getting that problem when they go to ship. So we want to help avoid those problems and prevent those kind of issues when they're going to market

And there's this constant fear that they cannot talk on the Slack channels or emails that freely anymore because LMS can consume all the data and much easier to figure out what you said before you didn't care because like, oh, they're collecting so much data, be just too difficult for them to go through all of it. But now you cannot have personal conversations on work forum anymore. Uh, so yeah, it is making a lot of difference in our day-to-day life. Our habits are changing.

We are just not aware about it.

And I would argue that neither can humans but. idea, and I think I was at an IDC conference a year ago. They kind of painted this picture of personal assistance, right? And the idea that you could have this assistant that has access to everything in your life and it knows your preference on where you want to sit on the plane and what you're willing to spend those constraints. But then also can map out how long it takes for you to walk from one spot to the other.

So it doesn't order the Uber for you until you get a little bit closer and then it orders that for you. I think that with all that potential, risk is that we become maybe not too dependent, but maybe a little bit lazy. Once it starts doing everything for us. A lot of people talk about it unlocking new. Time and availability for us. but I don't know. I don't know. Um, you know, if you've seen the movie Wally, it's kids movie.

That's a little scary for me floating around in a chair drinking soft drinks and outer space while everything goes to hell. And I'm like, is that what agents are gonna be in the future? What do you think?

And so when you get to doing those kind of menial or travel planning, like things, know, while it might've been fun in the past when I had less stresses on my life, it's not fun right now. You just want to get it done and you want something that truly understands, you know, all of the things that you're looking for and take care of that for you. in our our resource constrained environments, having something like an assistant, being able to do that for you reliably is super. Attractive.

And I think that will be quite beneficial for society in general because there we don't have enough people to get the work done. And so, that's, you know, having an assistant be able to do those kinds of things and then have people doing higher order work is much more desirable.

So, for example, if I don't have to deal with travel planning, I have all sorts of time that I can spend thinking about something far more creative and far more interesting, that, that's much more beneficial to myself or my family or the company I'm working for.

I read this one comment on Reddit and they were like, oh, soon we will be able to take any movie and ask it to be converted into a certain style and I can watch the same movie in a hundred different styles. That was mind blowing and that is not something that can be done by human. So you are consuming the same plot, but with different expression, different kind of people. You can even change the character that you want that person to play.

So that's a different way of thinking about entertainment. The other that obviously, uh, both Drew and you kind of data about the AI system doing the task so that we don't have to do manual stuff like ordering things on time, taking calling a plumber, making sure that everything is fine after they're gone. All the small, small things that we do on a daily basis. Like if that work gets taken away, we'll have a lot of time. And the last thing he said that, oh, we might be in now world like Wally.

Technically we can be, but with all the geopolitical things that we have today, it's very hard where a government is paying for you to just relax. That is never going to have happen in pretty much any part of the world. So that means if you want to have a job, you have to find a way to stand out. Everyone gets a job because there, there is a demand and there is supply. So if everyone is using ai, what can you do different that produces better results with AI that anyone else cannot do?

So I think people will get creative because we need jobs. So yes, we'll get lazy in certain part of the life, but at the end of the day, we still need to work. And I don't know what that kind of jobs would look like. For instance, when YouTube and everything came around, influencers became a job, right? That was not a job. 20, 30 years ago, people found a way to make money out of it. So we'll see some more creative jobs coming out of it.

So is the human as well as agent coexistence is what I think as more augmentation of the human rather than a replacement of the human Wally was a great movie, but I think that really exists as like the bookend of utopia. So, uh, that's the way I see it,

Okay. So recently we saw a couple of announcements and I think maybe everybody's on the same page that they need to work together. Anthropic released this concept of MCP, right? MCP came out. It doesn't mean everybody was all in it, but next thing you know, it's blowing up all over X and then there's this massive proliferation of MCP servers. This guy nuttle his website, playbooks.com, he's got 3,900 servers in a directory on that website, right?

So what's so great about MCP and what does it actually do for the agents?

with Octo, having those tool cards available to you so that you can go get data from systems and then process that through LLM is actually, a key enabler for any sort of agentic solution. Because without that context, you mentioned that earlier, Dan you, and that's context to me of, real data that's coming from real stuff, whether that be travel data or enterprise ID data, or you name it. It's all real data about what's going on in the world. You can't really make an agent solution successful.

So MCP unlocks that kind of real data context for us. A two A is interesting because now it gives us a standard way of interfacing with other autonomous agents. So MCP is all about getting data and interacting with the data. MCP or A to a is really more about how do I interact with other agents? And this is also work that we had in our roadmap that we wanted to get into because we see our system itself is gonna be an agent to others we'll be. like the subservient chicken, fine.

A lot of vendors don't want to be that. But new modern vendors will absolutely wanna be consumed by others as agents. And so we're off to doing our work independently and autonomously. You set us up to do all that work, and then we can feed and interact with other agents who may benefit from our work. That's a fantastic scalable approach to designing modern systems.

So, I think a two A is, you know, a welcome entry here it's taking a lot of bespoke work that a lot of people were doing independent of one another and making it standardized.

So this is a fantastic architectural approach to unlocking enormous value by letting these autonomous systems do the things that they're really good at. And it's going to require, vendors to get on board.

Unfortunately, a lot of the legacy vendors don't wanna get into the A to a thing because of the threat to their, platform models and, bringing people onto their platforms and trying to entrap or en encase their data and in boats, that's there's gonna be resistance in the legacy to their detriment by the way, they will they'll end up getting surpassed by modern entrance.

It's not unexpected to see novel attacks and novel approaches to using it to do nefarious things or potentially nefarious things. If there's potentially some design improvements that could be done there. I've been particularly concerned that the observability and auditability of what an agent or an MCP, connection is doing is a critical concern for the enterprise. They need to know what these things are up to for audit and compliance purposes, as well as security purposes.

And then the identity and control and access controls, needs to be addressed. We have a very simple approach. Concept there, because our MCP approach is that a user having the agent do work for them, doesn't get access to data. They wouldn't have access if they went directly to the system. So we're not trying to create an overlay additional privilege access management layer. We're using the existing privilege access management layer that's in the underlying data source to do that for us.

And that's a good lightweight concept that makes MCP lot simpler to implement. But other MCP approaches are trying to implement an entire privilege access, authorization layer inside of the MCP server itself, which is gonna be duplicative of what all of these data sources already have and gonna create holes and gaps and coverage and unintended consequences there. So think we've got some architectural work to do. We've gotta go back to the gym. make that more mature. But Mc p's moving so fast.

It started with you know, standard out in input output, got went to server side events and that was in, into streaming HT TP and, and in a month. It's, it's the, and, and OAuth is now put into to context and on and on and on, like the inva innovation here. People are listening the community. I see a community of people doing this development is just amazing. And, we're just drafting on that tailwind because don't have the, we're five people in this startup.

I can't go build something as big as this, but this community is built something which is just awesome.

The second thing that happened that actually blew up is the announcement that OpenAI and Gemini might start using it. So there were a lot of tool calling softwares out there. MCP was not the only one, but there was no standardized approach that everyone would be using this one technology.

Suddenly the fact that MCP had approval from most of the biggest player, that's why it drew up, because now people were like, okay, even if it doesn't per work perfectly, the fact that everyone else is using it and the community as a whole is contributing to it, I can depend on it. I can create my software around it. It's not gonna break because this is going to keep getting better and better. Uh, the other thing I wanna say, just a distinction. So MCP is think about a code.

That directly talks to your databases that can receive, prompt and return you back something. So one of the reason people like MCP is there's a deterministic approach to your MCP server that otherwise is lost in agent to agent. So if you are asking another agent, Hey, can you tell me what my sales quota looked like last quarter? You cannot have 2.1 or 2.5 if your thing was 2.6 million, right? You need to have accurate data. So where wherever you want accuracy, MCP becomes a thing.

So in, in the best world, you'll have an agent that always refers to an MCP to get their data needs and the other agent ask this agent so that that data can be combined in different ways based on the context or the prompt they're receiving. So that's where a two A comes in and MCP is just to make sure that you don't mess up data that you're get getting from different places.

Options to MCP, but because we're already down the path and people are able to adopt it very quickly, and it with a pretty low barrier to technical entry, especially if you're using coding assistance it seems very, very easy from what I've observed to be able to implement it. So I guess I could see how that would drive it, even though these announcements, they seem a little bit superficial to me. But fine. It is what it is. Right.

Okay. Shifting gears again let's talk a little bit about the ethical implications and challenges agents. Right. So one thing I'll mention again. So there is this. Paper that was published it's authored by Margaret Mitchell and Ava, gosh, both of hugging face. And basically what it says is we should not produce fully autonomous agents.

So that's kind of a counterpoint to what we've been hearing where people talk about, okay, we'll start with human in the loop and then we'll have human on the loop, which is the idea of supervising a team of agents, but going fully autonomous. We have clashing views here. I dunno what you've heard, Viby, but, I mean, it scares me a little growing up in the era of movies where robots take over the world, but that could just be a generational thing for me.

But at the same time, I could see how having fully autonomous agents who are performing tasks that you don't feel like there's a lot of risk could be beneficial. What do you think?

We need to sort of like set a taxonomy for the agent autonomy so that like we can understand the risks better and also put in control mechanisms to man to manage it. And also like how do we verify some of the safety related aspects, right? So in order to drive those aspects, the paper is making a claim towards the fact that there is no clear benefit only coming out from fully autonomous AI agents. We need to, there's also foreseeable harm that exists with those, so we need to manage them better.

Right. That is the call to action coming out from that paper. Um, need to, and drew, uh, your thoughts on those.

So having an autonomous agent do something without check-in balances, obviously it's not gonna work. If you have the right check-in balances, then it shouldn't be that much of a problem. The only difference though, is we as humans, when we grow up, we have a lot of environment conditioning. Like the fact that we understand, well, this is ethical and this is not ethical. Or for, like, I, I gave a very simple example. So suppose if you have an agent that is supposed to avoid churn.

If it learns that it doesn't show people who churn in the report, then the churn rate is low. I mean, it achieved its goal, right? But that's not an ethical way to do things. So for an agent to understand those small nuances that we human do, that's why they are more dangerous than a human is. And you'll always need some kind of human oversight no matter how smart they get.

Just because the way we perceive the world and the way we want to exist in this world is going to be always different than the way agents exist in the world. Drew, what do you think?

May be some may, maybe in some cases that's an accident investigation, right? Like that's the sad, unhappy path, right? Because something went wrong in the way some agent was behaving in a, in a vehicle. You know, but the, having the advantages of having these autonomous agents probably vastly outweigh the disadvantages or the concerns provided that the controls are there.

So if we get to a point where these agents are working in a fairly autonomous environment and they screw something up, who is accountable?

So it, it paid the merchant before receiving the money, and if something went wrong, they kind of managed both the, it's like a broker kind of thing That makes more sense than the ethical consideration of who's gonna take a blame. Because I don't think anyone is gonna take a blame when agents go, Hey, is it the company that's providing the model? Today's element, it can be something else. Or is it the data it was trained on? Is it the prompt that someone wrote?

It's a very hard question, and I think the easiest way would be to have an insurance broker in between.

And that, that's, so our opinion as we're building out this thing is, it is we're providing soft output reports and, insights into enterprise data. We could take actions, right? But we are taking a wait and c approach there because a, the risk tolerance of our customers may not accept us going and making changes to live production systems based on information that we discern. As they get more comfortable with it. Will they do that? I am pretty sure they will. How long is that gonna take?

We love to say that AI is moving really, really fast. But I think that, if you look at autonomous driving and the complexity there and how long it's taken us, and the lack of completion of that you know, our, all of the starry-eyed prognosticators that we're saying it was gonna happen in a year, and we're all gonna be driven to work by, by, by our car, and we can have a cup of coffee and relax, hasn't happened yet. not reliably and not, to the level that people are.

So I think that adoption is gonna have a long curve ahead of us. These systems are of equal complexity that I deal with. I think other human systems are gonna be of equal complexity. But there's tremendous opportunity for us to all learn and really build that better.

And then evolve over time, depending on how technology, as well as frameworks evolve. the gist that I'm hearing. In terms of like, let's not get bogged down by the, you know, all the risks, et cetera. Let's look at what's possible and let's kind of like move with optimism.

So right now we have a lot of websites, right? We, if I want to find an article, I have to go and search it and I have to click a couple of buttons. Sometimes I have to Google, Hey, how do I go and do this? That wouldn't exist. Your entire website could be recreated or shown based on your conversations. If I want to cancel. My subscription, you know, I can, Hey, how do I cancel? It brings up the page that I want.

There'll still be some visual elements, but what I'm seeing, I don't see us clicking through everything. And it also might be possible that the webpage design itself can be very different. What I see versus what you see might be very different. And that's in a different era of how the businesses are done and everything is sold. The lines between marketing, sales, creative writing, engineering product, everything is going to blur.

Um, yeah, I'm very clear about that and I do see that changing the entire life cycle of the product. Its itself.

The human condition, it's gonna, it's gonna come out in a number of different forms and things that I can't wait to see what this community innovates on because it's all to, to be seen. We're, we are just at the very tip of the iceberg this. So, I'm excited to see it, but I, I don't dare say where it's gonna go,

David Linthicum, well-known cloud guy also into a lot of AI stuff. Now. He said the other day, is there an ROI in agents? And it got me thinking a little bit about, well, it depends on how expensive the it is, or how cheap it is for a human to perform a task, So we went through this whole era of, outsourcing and offshoring and trying to drive down the cost of technology, right? And as we know, are still pretty expensive.

So if you take a use case like a coding assistant, or you can generate lot of code and it's relatively good, does it save you the money of the coding assistant doing the job of that developer, and now you don't have to pay that developer. Or maybe you can do it with two developers instead of 10. Okay. Fair. Right, because again, developers are expensive, call center agents as a kind of counterpoint are not as expensive.

So are you gonna see the same ROI or are you gonna see sufficient ROI for moving to those types of agents? If you were to replace call center representatives with agents

they flipped the ratio from 95% manual handling of those things to 95% automated by using. An AI design system that is essentially an ag agentic AI system. What's the human implication there? Right? You have contact centers and people doing paperwork, task work that are high turnover. The least. I mean, is they turned over people every four months on average. You know, you can't, people can't keep people in these jobs, so they're able to do the work.

Then they might have needed, a hundred people and now they need 10, right? they flip the ratio they're able to do with 10 people. Those 10 people are making a little more money 'cause they're actually doing higher value work and ha and handling the true escalations, better and more aggressively. So custom, meanwhile the customer experience is going up into the right. So ROI. Is a really interesting thing. Right. And it actually comes back to most enterprises.

That's where I'm working in, don't really do full, full bore TCO analysis that would drive their ROI setups and they tend to be cherry-picking in a lot of cases and it becomes political. And so I would say yes for sure. There are use cases that have ROI absolutely. Money back guarantee.

The reason I wanna talk to a person is because whatever constrained choices they have for me, first I have to listen through all the press, one for this, press two for this. And it might not even, I might go through all that thing and find out that they don't have answer to what I'm looking for. And that's when I wanna go to human, because I feel it's gonna be faster. Or they might have certain information that the compromised version is not gonna have.

So if you can flip the script, like if that when I'm calling someone can give me all the answers that I need and it's accurate, we are becoming very as humans, we are actually becoming more introvert. We don't wanna talk to other person. People don't take calls, people like messaging. So we will prefer not to talk to human unless we feel like the humans have certain information for instance, okay.

If there is a, so, so something got just launched, there is a good chance that the people in customer support doesn't know about that product as much yet, or the bugs as much yet. So the agent can do that much better because it's just, you know, they get the data and they have all the recent knowledge. Human people need to be trained for it.

But if I feel like, oh, something really bad is happening in the com company and they really need to understand from a human, get some kind of inclination what might be happening, I will probably call a customer agent because they might know if there is something down or they might know from other conversation what might be happening so they can co-create assumptions that software systems cannot. So yeah, I mean, there'll be a huge ROI just to switch away for whatever questions.

We don't actually need to talk to a human. Everything else can be, uh, automated.

And the talking to a human means you're sitting in an on hold queue. You're wasting time. You have to get them up to speed on what's going on. Half hour later you're still not getting an answer that you want. And that's, if you can do a lot of stuff in self-service, it is better in general for the customer. And that overall, ROI, which to me is related to TCO becomes much higher. Totally agree. Me to like, and the psychology hasn't changed.

I don't think it's actually changed as much as you're asserting. That would be the only place I would push back a little bit as I think this has been human behavior for a long time.

They had introduced at this pharmacy the notion of, oh, well if you can't be helped, you can leave a message. So you don't have to talk to a person. You can leave a message and it will forward that message to the pharmacist and then you can choose to get a call back or not if they're able to solve your problem entirely. what happened this last time when I called was something different.

So in the past I had sent the voice message and then I got a text saying, oh, your prescription's being filled. No problem. This time it sent me back a summary of the message that I left. So that meant that at some point, some system. what that was and pass that along to me and presumably pass it along to the pharmacist. So we're seeing little iterative improvements in these systems where there's this interpretation of the communication you're trying to send forward.

And so I, that was kind of a nice feature that, yeah, to that point, it did make me want to keep going with that pharmacy because it got a little bit easier, Well, we've taken up a lot of your time, we really appreciate here. I think that we'll probably wrap up. You know, today we talked about ag Gentech, ai, we talked about its applications, ethical concerns. What is it gonna look like in the future? Any kind of parting thoughts before we wrap up?

So That approach agentic AI to do tasks like that because they're with so much knowledge and ability and context, unlike any system, you know, an IVR system that's ever existed on the planet customer experiences will be much higher and I think the tolerance for interaction with agents will go up higher because the experiences will be better.

So yeah, I mean we, I don't know what kind of jobs will come in future. It'll be very interesting to see probably working with agents when it hallucinates or oversighting agents. I don't know. Maybe it'll allow people to write novels that were really bad at writing or create children books that they couldn't do before. So what is gonna change? And I don't know what looks like, but humans will always have jobs.

I don't think we, I see a future where we will not have jobs and agents are doing everything.

And really put in place the governance frameworks, the control mechanisms, et cetera, that's necessary to harness the potential out of any invention, any tool. In this case, AI agents, right? some of the examples that were mentioned were very realistic as well as like, you know, something that everybody can relate to, including a pharmacy example, right? So I think it, it for almost comes down to this, if we like factor in the labor economics, the ROI.

Provide a path for implementation of ai agents. It can take you know, that there is massive transformative potential associated with it, and we definitely should get on with it. Right? Not so much as a job replacement who thought about prompt engineering as a job like five years back, right? None of us con, contemplated that.

So there is definitely like shift in economics, in labor economics that might come about, like job descriptions and things like that, but certainly an avenue that we should you know, go forth with. I see.